Electronic equipment, estimation system, estimation method and estimation program

Abstract

To provide electronic equipment capable of easily estimating a subject's health condition, an estimation system, an estimation method and an estimation program.SOLUTION: Electronic equipment 100 comprises a control part 143 that estimates a subject's blood glucose level on the basis of the subject's age and an estimated formula created on the basis of a blood glucose level and the subject's pulse wave corresponding to the blood glucose level. The control part 143 uses the estimated formula, which is created on the basis of the result of a regression analysis using the subject's age and an index AI expressed by the ratio between an advancing wave of the pulse wave and a reflection wave appearing later than the advancing wave.SELECTED DRAWING: Figure 10

Description

[Technical field]

[0001] The present invention relates to an electronic device, an estimation system, an estimation method, and an estimation program for estimating a health condition of a subject from measured biological information. [Background technology]

[0002] Conventionally, blood components and blood fluidity have been measured as a means for estimating the health condition of a subject (user). These measurements are performed using blood drawn from the subject. Electronic devices that measure biological information from a test site such as the subject's wrist are also known. For example, Patent Document 1 describes an electronic device that measures the subject's pulse by being worn on the subject's wrist. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-360530 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, drawing blood is painful, making it difficult to estimate one's own health condition on a daily basis. In addition, the electronic device described in Patent Document 1 only measures the pulse rate, and cannot estimate the subject's health condition other than the pulse rate.

[0005] In view of the above circumstances, an object of the present invention is to provide an electronic device, an estimation system, an estimation method, and an estimation program that can easily estimate the health state of a subject. [Means for solving the problem]

[0006] In order to solve the above problems, an electronic device according to one embodiment of the present invention includes a control unit that estimates the blood glucose level of a subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the age of the subject. The control unit uses, as the estimation equation, an estimation equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

[0007] Moreover, an electronic device according to one embodiment of the present invention includes a control unit that estimates the glucose metabolism of the subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the age of the subject. The control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

[0008] Moreover, an electronic device according to one embodiment of the present invention includes a control unit that estimates the lipid level of the subject based on an estimation equation created based on the lipid level and the subject's pulse wave associated with the lipid level, and the age of the subject, and the control unit uses as the estimation equation an equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave.

[0009] Moreover, an electronic device according to one embodiment of the present invention includes a control unit that estimates the lipid metabolism of the subject based on an estimation equation created based on lipid values ​​and the subject's pulse waves associated with the lipid values, and the age of the subject, and the control unit uses as the estimation equation an equation created based on the results of a regression analysis using the subject's age and an index AI represented by the ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave.

[0010] Moreover, an estimation method according to one embodiment of the present invention includes an estimation step of estimating the blood glucose level or glucose metabolism of the subject based on an estimation equation created based on the blood glucose level and the subject's pulse wave associated with the blood glucose level, and the age of the subject, and the estimation step uses, as the estimation equation, an estimation equation created based on the results of a regression analysis using the age of the subject and an index AI represented by the ratio between the forward wave of the pulse wave and the reflected wave that appears later than the forward wave.

[0011] Moreover, an estimation method according to one embodiment of the present invention includes an estimation step of estimating a lipid level or lipid metabolism of a subject based on an estimation equation created based on a lipid level and a pulse wave of the subject associated with the lipid level, and the age of the subject, wherein the estimation step uses an estimation equation created based on the results of a regression analysis using the age of the subject and an index AI represented by the ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave.

[0012] Furthermore, an estimation program according to one embodiment of the present invention is an estimation program that causes a computer to function as a control unit that estimates a blood glucose level or glucose metabolism of a subject based on an estimation equation created based on a blood glucose level and a pulse wave of the subject associated with the blood glucose level, and the age of the subject, and the control unit uses, as the estimation equation, an equation created based on the results of a regression analysis using the age of the subject and an index AI represented by the ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave.

[0013] Furthermore, an estimation program according to one embodiment of the present invention is an estimation program that causes a computer to function as a control unit that estimates the lipid level or lipid metabolism of a subject based on an estimation equation created based on a lipid level and a pulse wave of the subject associated with the lipid level, and the age of the subject, and the control unit uses as the estimation equation an estimation equation created based on the results of a regression analysis using the age of the subject and an index AI represented by the ratio between a forward wave of the pulse wave and a reflected wave that appears later than the forward wave. Effect of the Invention

[0014] According to the present invention, it is possible to provide an electronic device, an estimation system, an estimation method, and an estimation program capable of easily estimating the health condition of a subject. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. [Diagram 2] 2 is a cross-sectional view showing a schematic configuration of a main body part of FIG. 1. [Diagram 3] FIG. 2 is a diagram showing an example of a state in which the electronic device in FIG. 1 is used. [Figure 4] 2 is a functional block diagram showing a schematic configuration of the electronic device of FIG. 1. [Diagram 5] 2 is a diagram illustrating an example of an estimation method based on a change in a pulse wave in the electronic device of FIG. 1. [Figure 6] FIG. 4 is a diagram showing an example of an acceleration pulse wave. [Figure 7] FIG. 4 is a diagram showing an example of a pulse wave acquired by a sensor unit. [Figure 8] 1. FIG. 4 is a diagram illustrating another example of an estimation method based on a change in a pulse wave in the electronic device of FIG. [Figure 9] 2 is a flow chart for creating an estimation formula used by the electronic device of FIG. 1. [Figure 10] FIG. 10 is a flow diagram for estimating a subject's postprandial blood glucose level using the estimation equation created by the flow of FIG. [Figure 11] FIG. 10 is a diagram showing a comparison between postprandial blood glucose levels estimated using the estimation equation created by the flow of FIG. 9 and actually measured postprandial blood glucose levels. [Figure 12] FIG. 11 is a flow chart for creating an estimation equation used by an electronic device according to a second embodiment of the present invention. [Figure 13] FIG. 13 is a flow diagram for estimating a subject's postprandial blood glucose level using the estimation equation created by the flow of FIG. 12. [Figure 14] FIG. 11 is a flow chart for creating an estimation equation used by an electronic device according to a third embodiment of the present invention. [Figure 15]FIG. 15 is a flow diagram for estimating the postprandial lipid level of a subject using the estimation equation created by the flow of FIG. 14. [Figure 16] FIG. 15 is a diagram showing a comparison between postprandial lipid levels estimated using the estimation equation created by the flow of FIG. 14 and actually measured postprandial lipid levels. [Figure 17] FIG. 2 is a diagram illustrating communication between an electronic device and a blood glucose meter. [Figure 18] 1 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0017] (First embodiment) Fig. 1 is a schematic diagram showing a schematic configuration of an electronic device according to a first embodiment of the present invention. Electronic device 100 includes a mounting unit 110 and a measurement unit 120. Fig. 1 is a view of electronic device 100 observed from a back surface 120a that comes into contact with a test portion.

[0018] The electronic device 100 measures biological information of the subject while the subject is wearing the electronic device 100. The biological information measured by the electronic device 100 is the pulse wave of the subject that can be measured by the measurement unit 120. In the present embodiment, the electronic device 100 will be described below as being worn on the wrist of the subject and acquiring the pulse wave, as an example.

[0019] In this embodiment, the wearing unit 110 is a straight, elongated band. The pulse wave is measured, for example, with the subject wearing the wearing unit 110 of the electronic device 100 around his / her wrist. Specifically, the subject wraps the wearing unit 110 around his / her wrist so that the back surface 120a of the measuring unit 120 comes into contact with the part to be measured, and measures the pulse wave. The electronic device 100 measures the pulse wave of blood flowing through the ulnar artery or radial artery at the subject's wrist.

[0020] Fig. 2 is a cross-sectional view showing a schematic configuration of the measurement unit 120 in Fig. 1. In Fig. 2, the attachment unit 110 around the measurement unit 120 is also shown in addition to the measurement unit 120.

[0021] The measuring unit 120 has a back surface 120a that contacts the subject's wrist when worn, and a front surface 120b opposite to the back surface 120a. The measuring unit 120 has an opening 111 on the back surface 120a side. The sensor unit 130 is supported by the measuring unit 120 with one end protruding from the opening 111 to the back surface 120a side when the elastic body 140 is not pressed. One end of the sensor unit 130 is provided with a pulse contact part 132. One end of the sensor unit 130 is displaceable in a direction approximately perpendicular to the plane of the back surface 120a. The other end of the sensor unit 130 is supported by the measuring unit 120 by a support part 133 so that one end of the sensor unit 130 is displaceable.

[0022] One end of the sensor unit 130 is in contact with the measurement unit 120 via the elastic body 140 and is displaceable. The elastic body 140 is, for example, a spring. However, the elastic body 140 is not limited to a spring and can be any other elastic body, for example, a resin, a sponge, or the like.

[0023] Although not shown, the measurement unit 120 may also include a control unit, a storage unit, a communication unit, a power supply unit, a notification unit, circuits for operating these units, connecting cables, and the like.

[0024] The sensor unit 130 includes an angular velocity sensor 131 that detects the displacement of the sensor unit 130. The angular velocity sensor 131 may be capable of detecting the angular displacement of the sensor unit 130. The sensor included in the sensor unit 130 is not limited to the angular velocity sensor 131, and may be, for example, an acceleration sensor, an angle sensor, or another motion sensor, or may include a plurality of these sensors.

[0025] The electronic device 100 includes an input unit 141 on the surface 120b side of the measurement unit 120. The input unit 141 receives operation input from the subject, and is configured, for example, with an operation button (operation key). The input unit 141 may be configured, for example, with a touch screen.

[0026] 3 is a diagram showing an example of a state in which the electronic device 100 is used by a subject. The subject uses the electronic device 100 by wrapping it around the wrist. The electronic device 100 is worn with the back surface 120a of the measuring unit 120 in contact with the part to be measured. With the wearing unit 110 wrapped around the wrist, the position of the measuring unit 120 can be adjusted so that the pulse contact unit 132 comes into contact with the position where the ulnar artery or radial artery is present.

[0027] In FIG. 3, when the electronic device 100 is worn, one end of the sensor unit 130 is in contact with the skin on the radial artery, which is an artery on the thumb side of the subject's left hand. One end of the sensor unit 130 is in contact with the skin on the radial artery of the subject due to the elastic force of the elastic body 140 arranged between the measurement unit 120 and the sensor unit 130. The sensor unit 130 is displaced according to the movement of the radial artery of the subject, i.e., pulsation. The angular velocity sensor 131 detects the displacement of the sensor unit 130 to obtain a pulse wave. The pulse wave is a waveform obtained by capturing the volume change over time of a blood vessel caused by the inflow of blood from the body surface.

[0028] Referring again to FIG. 2, the sensor unit 130 has one end protruding from the opening 111 when the elastic body 140 is not pressed. When the electronic device 100 is attached to the subject, one end of the sensor unit 130 is in contact with the skin on the subject's radial artery, and the elastic body 140 expands and contracts in response to pulsation, displacing the one end of the sensor unit 130. The elastic body 140 has an appropriate elastic modulus so as not to impede pulsation and to expand and contract in response to pulsation. The opening width W of the opening 111 is sufficiently larger than the blood vessel diameter, which is the radial artery diameter in this embodiment. By providing the opening 111 in the measurement unit 120, the rear surface 120a of the measurement unit 120 does not press the radial artery when the electronic device 100 is attached. Therefore, the electronic device 100 can acquire a pulse wave with less noise, improving the accuracy of the measurement.

[0029] 3 shows an example in which the electronic device 100 is worn on the wrist and a pulse wave in the radial artery is acquired, but the present invention is not limited to this. For example, the electronic device 100 may acquire a pulse wave of blood flowing through the carotid artery in the subject's neck. Specifically, the subject may measure the pulse wave by lightly pressing the pulse application unit 132 against the position of the carotid artery. The subject may also wear the attachment unit 110 by wrapping it around the neck so that the pulse application unit 132 is located at the position of the carotid artery.

[0030] 4 is a functional block diagram showing a schematic configuration of electronic device 100. Electronic device 100 includes sensor unit 130, input unit 141, control unit 143, power supply unit 144, storage unit 145, communication unit 146, and notification unit 147. In this embodiment, control unit 143, power supply unit 144, storage unit 145, communication unit 146, and notification unit 147 are included inside measurement unit 120 or wearing unit 110.

[0031] The sensor unit 130 includes an angular velocity sensor 131, and detects pulsation from the subject site to obtain a pulse wave.

[0032] The control unit 143 is a processor that controls and manages the entire electronic device 100, including each functional block of the electronic device 100. The control unit 143 is also a processor that estimates the blood glucose level of the subject from the acquired pulse wave. The control unit 143 is composed of a processor such as a CPU (Central Processing Unit) that executes a program that defines a control procedure and a program that estimates the blood glucose level of the subject, and such programs are stored in a storage medium such as the storage unit 145. The control unit 143 also estimates the state of the subject regarding glucose metabolism or lipid metabolism, etc., based on the calculated index. The control unit 143 notifies the notification unit 147 of data.

[0033] The power supply unit 144 includes, for example, a lithium ion battery and a control circuit for charging and discharging the battery, and supplies power to the entire electronic device 100 .

[0034] The storage unit 145 stores programs and data. The storage unit 145 may include any non-transitory storage medium, such as a semiconductor storage medium and a magnetic storage medium. The storage unit 145 may include a plurality of types of storage media. The storage unit 145 may include a combination of a portable storage medium, such as a memory card, an optical disk, or a magneto-optical disk, and a storage medium reader. The storage unit 145 may include a storage device used as a temporary storage area, such as a RAM (Random Access Memory). The storage unit 145 stores various information and programs for operating the electronic device 100, and also functions as a work memory. The storage unit 145 may store, for example, a measurement result of a pulse wave acquired by the sensor unit 130.

[0035] The communication unit 146 transmits and receives various data by performing wired or wireless communication with an external device. For example, the communication unit 146 communicates with an external device that stores biological information of a subject to manage the health condition, and transmits the measurement results of the pulse wave measured by the electronic device 100 and the health condition estimated by the electronic device 100 to the external device.

[0036] The notification unit 147 notifies information by sound, vibration, image, etc. The notification unit 147 may include a speaker, a vibrator, and a display device such as a liquid crystal display (LCD), an organic electro-luminescence display (OELD), or an inorganic electro-luminescence display (IELD). In the present embodiment, the notification unit 147 notifies, for example, the state of glucose metabolism or lipid metabolism of the subject.

[0037] The electronic device 100 estimates the blood glucose level of the subject based on an estimation equation created by regression analysis. The electronic device 100 stores the estimation equation for estimating the blood glucose level based on the pulse wave in, for example, the storage unit 145 in advance. The electronic device 100 estimates the blood glucose level using these estimation equations.

[0038] Here, the estimation theory for estimating blood glucose level based on pulse wave will be described. After a meal, blood glucose level rises, which causes a decrease in blood fluidity (increase in viscosity), dilation of blood vessels, and an increase in circulating blood volume, and vascular dynamics and hemodynamics are determined so that these states are in equilibrium. The decrease in blood fluidity occurs, for example, due to an increase in the viscosity of plasma or a decrease in the deformability of red blood cells. In addition, vascular dilation occurs due to the secretion of insulin, the secretion of digestive hormones, an increase in body temperature, and the like. When blood vessels dilate, the pulse rate increases to suppress a decrease in blood pressure. In addition, an increase in circulating blood volume compensates for blood consumption for digestion and absorption. Changes in vascular dynamics and hemodynamics before and after a meal due to these factors are also reflected in the pulse wave. Therefore, the electronic device 100 can acquire a pulse wave and estimate a blood glucose level based on changes in the waveform of the acquired pulse wave.

[0039] Based on the above estimation theory, an estimation formula for estimating blood glucose level can be created by performing regression analysis based on sample data of pre- and post-prandial blood glucose levels and pulse waves obtained from multiple subjects. During estimation, the blood glucose level of the subject can be estimated by applying the created estimation formula to an index based on the subject's pulse wave. In particular, by performing regression analysis using sample data whose blood glucose level variation is close to a normal distribution, the blood glucose level of the subject to be tested can be estimated regardless of whether it is before or after a meal.

[0040] 5 is a diagram for explaining an example of an estimation method based on a change in a pulse wave, showing an example of a pulse wave. An estimation formula for estimating a blood glucose level is created by, for example, a regression analysis of an index (rising index) Sl indicating the rise of a pulse wave, an Augmentation Index (AI), and a pulse rate PR.

[0041] The rise index Sl is derived based on the waveform shown in area D1 of Fig. 5. Specifically, the rise index Sl is the ratio of the first minimum value to the first maximum value in an accelerated pulse wave derived by differentiating the pulse wave twice. The rise index Sl is expressed as -b / a in the accelerated pulse wave shown as an example in Fig. 6. The rise index Sl decreases due to a decrease in blood fluidity after a meal, the secretion of insulin, and the expansion (relaxation) of blood vessels due to an increase in body temperature.

[0042] AI is an index expressed by the ratio of the magnitude of the forward wave and the reflected wave of the pulse wave. A method of deriving AI will be described with reference to FIG. 7. FIG. 7 is a diagram showing an example of a pulse wave acquired at the wrist using electronic device 100. FIG. 7 shows a case where angular velocity sensor 131 is used as a pulsation detection means. FIG. 7 shows the time integration of the angular velocity acquired by angular velocity sensor 131, with the horizontal axis representing time and the vertical axis representing angle. Since the acquired pulse wave may contain noise due to, for example, the subject's body movement, correction may be performed using a filter that removes DC (Direct Current) components, and only the pulsation components may be extracted.

[0043] Pulse wave propagation is the phenomenon in which the pulsation of blood pushed out from the heart travels through the walls of arteries and blood. The pulsation of blood pushed out from the heart reaches the extremities of the limbs as a forward wave, and some of it is reflected at the branching of blood vessels and areas where the diameter of the blood vessels changes, and returns as a reflected wave. AI is the magnitude of this reflected wave divided by the magnitude of the forward wave, and is calculated as AI. n =(P Rn -P Sn ) / (P Fn -P Sn ) where AIn is the AI ​​for each pulse. For example, AI is calculated by measuring the pulse wave for several seconds and calculating the AI ​​for each pulse. n (n=integer from 1 to n) average value AI ave The AI ​​may be calculated based on the waveform shown in area D2 in Fig. 5. The AI ​​decreases after a meal due to a decrease in blood fluidity and an increase in body temperature that causes blood vessels to expand.

[0044] The pulse rate PR is the period T of the pulse wave shown in Figure 5. PR The pulse rate PR increases after a meal.

[0045] The electronic device 100 can estimate the blood glucose level using an estimation equation created based on the rising index Sl, AI, and pulse rate PR.

[0046] FIG. 8 is a diagram for explaining another example of an estimation method based on changes in a pulse wave. FIG. 8(a) shows a pulse wave, and FIG. 8(b) shows the result of FFT (Fast Fourier Transform) of the pulse wave in FIG. 8(a). An estimation equation for estimating blood glucose level is created, for example, by regression analysis of fundamental wave and harmonic components (Fourier coefficients) derived by FFT. The peak value in the FFT result shown in FIG. 8(b) changes based on changes in the waveform of the pulse wave. Therefore, blood glucose level can be estimated by an estimation equation created based on the Fourier coefficients.

[0047] The electronic device 100 estimates the blood glucose level of the subject using an estimation equation based on the above-mentioned rising index Sl, AI, and pulse rate PR, as well as the Fourier coefficients and the like.

[0048] Here, a method for creating an estimation formula used by the electronic device 100 when estimating a blood glucose level of a subject will be described. The estimation formula does not need to be created by the electronic device 100, and may be created in advance using another computer or the like. In this specification, the device that creates the estimation formula will be referred to as an estimation formula creation device. The created estimation formula is stored in advance, for example, in the storage unit 145, before the subject estimates the blood glucose level using the electronic device 100.

[0049] Fig. 9 is a flow diagram for creating an estimation formula used by the electronic device 100 of Fig. 1. The estimation formula is created by measuring the subject's pre- and post-prandial pulse waves using a pulse wave meter, and measuring the subject's pre- and post-prandial blood glucose levels using a blood glucose meter, and performing regression analysis based on sample data acquired by the measurements. Note that "pre-prandial" refers to the subject's fasting state, and "post-prandial" refers to the time a predetermined time after a meal when blood glucose levels rise (for example, about one hour after starting a meal). The sample data acquired is not limited to pre- and post-prandial data, and may be data from a time period when blood glucose levels fluctuate greatly.

[0050] In creating an estimation equation, first, information on the subject's pre-prandial blood glucose level and the pulse wave associated with the blood glucose level, measured by a blood glucose meter and a pulse wave meter, respectively, are input to an estimation equation creation device (step S101).

[0051] Furthermore, information on the subject's postprandial blood glucose level and the pulse wave associated with the blood glucose level, measured by a blood glucose meter and a pulse wave meter, respectively, are input to the estimation equation creation device (step S102). The blood glucose level input in steps S101 and S102 is measured by the blood glucose meter, for example, by taking a blood sample. Furthermore, in step S101 or step S102, the age of the subject of each sample data is also input.

[0052] The estimation formula creation device determines whether the number of samples of the sample data input in steps S101 and S102 is equal to or greater than N, which is sufficient for performing regression analysis (step S103). The number of samples N can be determined appropriately and can be set to, for example, 100. If the estimation formula creation device determines that the number of samples is less than N (if No), it repeats steps S101 and S102 until the number of samples becomes equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S104 and executes calculation of the estimation formula.

[0053] In calculating the estimation equation, the estimation equation creation device analyzes the input pre- and post-prandial pulse waves (step S104). In this embodiment, the estimation equation creation device analyzes the onset index Sl, AI, and pulse rate PR of the pre- and post-prandial pulse waves. The estimation equation creation device may also perform FFT analysis to analyze the pulse waves.

[0054] Then, the estimation formula creation device executes regression analysis (step S105). The objective variable in the regression analysis is the postprandial blood glucose level. The explanatory variables in the regression analysis are the age input in step S101 or step S102, and the preprandial and postprandial pulse wave onset indexes Sl, AI, and pulse rate PR analyzed in step S104. When the estimation formula creation device performs FFT analysis in step S104, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

[0055] The estimation formula creating device creates an estimation formula for estimating the postprandial blood glucose level based on the result of the regression analysis (step S106). An example of the estimation formula for estimating the postprandial blood glucose level is shown in the following formula (1).

[0056]

number

[0057] In formula (1), GLa indicates the postprandial blood glucose level. Furthermore, age indicates age, PRb indicates the preprandial pulse rate PR, AIb indicates the preprandial AI, Slb indicates the preprandial rise index Sl, PRa indicates the postprandial pulse rate PR, AIa indicates the postprandial AI, Sla indicates the postprandial rise index Sl, and BLG indicates the blood glucose level input by the subject (measured by drawing blood). The blood glucose level BLG input by the subject is a blood glucose level measured at a different timing from the estimated blood glucose level GLa. In this embodiment, the blood glucose level BLG input by the subject is a blood glucose level measured by drawing blood before a meal. By using the blood glucose level BLG measured by drawing blood in the estimation formula, the estimation accuracy of the blood glucose level is improved.

[0058] Next, a flow of estimating the blood glucose level of a subject using the estimation equation will be described. Fig. 10 is a flow diagram of estimating the postprandial blood glucose level of a subject using the estimation equation created by the flow of Fig. 9. Here, a case where the subject inputs the preprandial blood glucose level measured using a blood glucose meter from the input unit 141 of the electronic device 100 will be described.

[0059] First, the electronic device 100 inputs the age of the subject based on the subject's operation of the input unit 141 (step S201).

[0060] Furthermore, the electronic device 100 inputs the pre-prandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S202).

[0061] Furthermore, the electronic device 100 measures the subject's pre-prandial pulse wave based on the subject's operation (step S203).

[0062] After the subject has eaten, the electronic device 100 measures the subject's postprandial pulse wave based on an operation by the subject (step S204).

[0063] Next, the electronic device 100 analyzes the measured pulse wave (step S205). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

[0064] The electronic device 100 estimates the subject's postprandial blood glucose level by applying the preprandial blood glucose level input in step S202, the rise index S1, AI, and pulse rate PR analyzed in step S205, and the subject's age to, for example, the above-mentioned formula (1) (step S206). The estimated postprandial blood glucose level is notified to the subject by, for example, the notifying unit 147 of the electronic device 100.

[0065] FIG. 11 is a diagram showing a comparison between postprandial blood glucose levels estimated using the estimation equation created by the flow of FIG. 9 and actually measured postprandial blood glucose levels. In the graph shown in FIG. 11, the horizontal axis shows postprandial blood glucose measurements (actual measurements) and the vertical axis shows postprandial blood glucose estimates. The blood glucose measurements were taken using a blood glucose meter, MedisafeFit, manufactured by Terumo Corporation. As shown in FIG. 11, the measured and estimated values ​​are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation equation is within 20%.

[0066] In this way, the electronic device 100 can estimate the postprandial blood glucose level non-invasively and in a short time based on the preprandial blood glucose level measured by the subject through blood sampling. In this embodiment, the estimation formula is created using the preprandial and postprandial blood glucose levels and pulse waves, but the creation of the estimation formula is not limited to this, and the estimation formula may be created using either the preprandial or postprandial blood glucose level and pulse wave. Furthermore, the electronic device 100 may estimate the blood glucose level of the subject at any timing, not limited to the postprandial blood glucose level. The electronic device 100 can also estimate the blood glucose level at any timing non-invasively and in a short time.

[0067] The electronic device 100 according to the present embodiment may update the estimation formula stored in the storage unit 145 based on the subject's preprandial blood glucose level and pulse wave acquired in steps S202 and S203 in estimating the blood glucose level. That is, the electronic device 100 can use the preprandial blood glucose level and pulse wave acquired in estimating the blood glucose level as sample data for updating the estimation formula. As a result, the estimation formula is updated every time the subject estimates the blood glucose level, and the estimation accuracy of the postprandial blood glucose level using the estimation formula is improved.

[0068] (Second embodiment) In the first embodiment, a case where an estimation formula is created based on the subject's pre- and post-prandial blood glucose levels and pulse waves is described. In the second embodiment, an example where an estimation formula is created based on the subject's own pre- and post-prandial blood glucose levels and pulse waves is described.

[0069] 12 is a flow diagram of creating an estimation formula used by electronic device 100 according to the present embodiment. In the present embodiment, the estimation formula will be described as being created by electronic device 100. Note that the estimation formula may be created by an estimation formula creation device different from electronic device 100, as described in the first embodiment.

[0070] First, the electronic device 100 inputs the pre-prandial blood glucose level measured by the subject using a blood glucose meter based on the subject's operation of the input unit 141 (step S301).

[0071] Furthermore, the electronic device 100 measures the subject's pre-prandial pulse wave based on the subject's operation (step S302).

[0072] After the subject has eaten, the electronic device 100 inputs the postprandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S303). The blood glucose level input in steps S301 and S303 is measured by the blood glucose meter, for example, by the subject drawing blood.

[0073] Furthermore, the electronic device 100 measures the postprandial pulse wave of the subject based on an operation by the subject (step S304).

[0074] The electronic device 100 determines whether the number of samples of the sample data input in steps S301 to S304 is equal to or greater than N, which is sufficient for performing regression analysis (step S305). The number of samples N can be determined appropriately, and can be set to, for example, 5. If the estimation formula creation device determines that the number of samples is less than N (if No), it repeats steps S301 to S304 until the number of samples becomes equal to or greater than N. On the other hand, if the estimation formula creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S306 and executes calculation of the estimation formula.

[0075] The method of calculating the estimated equation in steps S306 to S308 is similar to steps S104 to S106 in Fig. 9, and therefore detailed description thereof will be omitted here. The estimated equation created by electronic device 100 according to the flow shown in Fig. 12 is, for example, an equation in which each coefficient is different in equation (1).

[0076] Next, a flow of estimating the blood glucose level of a subject using the estimation equation will be described. Fig. 13 is a flow diagram of estimating the postprandial blood glucose level of a subject using the estimation equation created by the flow of Fig. 12. Here, a case where the blood glucose level measured by the subject using a blood glucose meter is input from the input unit 141 of the electronic device 100 will be described.

[0077] First, the electronic device 100 inputs the age of the subject based on the subject's operation of the input unit 141 (step S401).

[0078] Furthermore, the electronic device 100 inputs the pre-prandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S402).

[0079] Furthermore, the electronic device 100 measures the subject's pre-prandial pulse wave based on the subject's operation (step S403).

[0080] After the subject has eaten, the electronic device 100 measures the subject's postprandial pulse wave based on an operation by the subject (step S404).

[0081] Next, the electronic device 100 analyzes the measured pulse wave (step S405). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

[0082] The electronic device 100 estimates the postprandial blood glucose level of the subject by applying the rising index Sl, AI, and pulse rate PR analyzed in step S405 and the subject's age to the estimation formula created in the flow diagram of Fig. 12 (step S406). The estimated postprandial blood glucose level is notified to the subject by, for example, the notifying unit 147 of the electronic device 100.

[0083] In this way, the electronic device 100 can non-invasively and quickly estimate the postprandial blood glucose level based on the preprandial blood glucose level measured by blood sampling from the subject. In this embodiment, the estimation formula for estimating the postprandial blood glucose level is created based on sample data obtained from the subject, improving the estimation accuracy of the postprandial blood glucose level of the subject.

[0084] Similarly to the first embodiment, in the electronic device 100 according to the present embodiment, the estimation formula stored in the storage unit 145 may be updated based on the subject's preprandial blood glucose level and pulse wave acquired in steps S402 and S403 in estimating the blood glucose level. This allows the estimation formula to be updated every time the subject estimates the blood glucose level, improving the accuracy of estimating the postprandial blood glucose level using the estimation formula.

[0085] Furthermore, when a sufficient number of sample data can be collected from the subject, the electronic device 100 may estimate the blood glucose level based on the subject's pulse wave, without using the blood glucose level measured by blood sampling. For example, the electronic device 100 estimates the subject's pre-prandial blood glucose level based on the subject's pre-prandial pulse wave. In this way, the subject uses the electronic device 100 to measure the pre-prandial pulse wave, and the electronic device 100 can estimate the subject's pre-prandial blood glucose level using an estimation formula based on the pre-prandial pulse wave. In this case, the electronic device 100 can also estimate the pre-prandial blood glucose level non-invasively and in a short time. Note that sufficient sample data refers to an amount of data that can create an estimation formula that can estimate the subject's pre-prandial blood glucose level with a predetermined accuracy or higher based on the pre-prandial pulse wave. Furthermore, the estimated blood glucose level is not limited to the pre-prandial blood glucose level, but the post-prandial blood glucose level may be estimated based on the post ... The estimated blood glucose level is not limited to the pre-prandial blood glucose level, but the blood glucose level at any timing may be estimated based on the pulse wave measured at any timing.

[0086] (Third embodiment) In the first embodiment, a case where the electronic device 100 estimates the postprandial blood glucose level of a subject has been described. In the third embodiment, an example of a case where the electronic device 100 estimates the postprandial lipid level of a subject will be described. Here, the lipid level includes neutral fat, total cholesterol, HDL cholesterol, LDL cholesterol, etc. In the description of this embodiment, the description of the same points as in the first embodiment will be omitted as appropriate.

[0087] The electronic device 100 stores in advance, for example, in the storage unit 145, estimation equations for estimating lipid levels based on pulse waves. The electronic device 100 estimates lipid levels using these estimation equations.

[0088] The estimation theory for estimating lipid levels based on pulse waves is the same as the estimation theory for blood glucose levels described in the first embodiment. That is, changes in lipid levels in blood are also reflected in changes in the waveform of the pulse wave. Therefore, the electronic device 100 can acquire a pulse wave and estimate a lipid level based on changes in the acquired pulse wave. The electronic device 100 improves the estimation accuracy of the lipid level by inputting the blood glucose level together with the pulse wave at the time of lipid estimation.

[0089] FIG. 14 is a flow diagram for creating an estimation formula used by electronic device 100 according to this embodiment. In this embodiment as well, the estimation formula is created by performing regression analysis based on sample data. In this embodiment, the estimation formula is created based on the pulse wave, lipid level, and blood glucose level before a meal as sample data. In this embodiment, "pre-meal" refers to the subject's fasting state. Furthermore, "post-meal" refers to the time when lipid levels are high a predetermined time after a meal (for example, about 3 hours after starting a meal). In particular, by performing regression analysis using sample data in which the variation in lipid levels is close to a normal distribution, the estimation formula can be used to estimate lipid levels at any timing of the subject to be tested, regardless of whether it is before or after a meal.

[0090] In creating an estimation equation, first, information regarding the subject's pre-prandial blood glucose level, as well as the pulse wave and lipid values ​​associated with the blood glucose level, measured by a blood glucose meter, a pulse wave meter, and a lipid measurement device, is input into the estimation equation creation device (step S501).

[0091] Furthermore, information on the subject's postprandial blood glucose level, and the pulse wave and lipid level associated with the blood glucose level, measured by the blood glucose meter, pulse wave meter, and lipid measurement device, respectively, are input to the estimation equation creation device (step S502). The blood glucose level input in steps S501 and S502 is measured by the blood glucose meter, for example, by taking a blood sample. Furthermore, in step S501 or step S502, the age of the subject of each sample data is also input.

[0092] The estimation equation creation device determines whether the number of samples of the sample data input in steps S501 and S502 is equal to or greater than N, which is sufficient for performing regression analysis (step S503). The number of samples N can be determined appropriately and can be set to, for example, 100. If the estimation equation creation device determines that the number of samples is less than N (if No), it repeats steps S501 and S502 until the number of samples becomes equal to or greater than N. On the other hand, if the estimation equation creation device determines that the number of samples is equal to or greater than N (if Yes), it proceeds to step S504 and executes calculation of the estimation equation.

[0093] In calculating the estimation equation, the estimation equation creation device analyzes the input pre- and post-prandial pulse waves (step S504). In this embodiment, the estimation equation creation device analyzes the onset index Sl, AI, and pulse rate PR of the pre- and post-prandial pulse waves. The estimation equation creation device may also perform FFT analysis to analyze the pulse waves.

[0094] Then, the estimation formula creation device executes regression analysis (step S505). The response variable in the regression analysis is the postprandial lipid level. The explanatory variables in the regression analysis are the age input in step S501 or step S502, and the preprandial and postprandial pulse wave onset indexes Sl, AI, and pulse rate PR analyzed in step S504. When the estimation formula creation device performs FFT analysis in step S504, the explanatory variables may be, for example, Fourier coefficients calculated as a result of the FFT analysis.

[0095] The estimation equation creating device creates an estimation equation for estimating postprandial lipid levels based on the results of the regression analysis (step S506).

[0096] Next, a flow of estimating a lipid level of a subject using the estimation equation will be described. Fig. 15 is a flow diagram of estimating a postprandial lipid level of a subject using the estimation equation created by the flow of Fig. 14. Here, a case where a blood glucose level measured by a blood glucose meter is input by the input unit 141 of the electronic device 100 will be described.

[0097] First, the electronic device 100 inputs the age of the subject based on the subject's operation of the input unit 141 (step S601).

[0098] Furthermore, the electronic device 100 inputs the pre-prandial blood glucose level measured by the subject using the blood glucose meter based on the subject's operation of the input unit 141 (step S602).

[0099] Furthermore, the electronic device 100 measures the subject's pre-prandial pulse wave based on the subject's operation (step S603).

[0100] After the subject has eaten, the electronic device 100 inputs the postprandial blood glucose level measured by the subject using the blood glucose meter based on the operation by the subject (step S604).

[0101] Furthermore, the electronic device 100 measures the postprandial pulse wave of the subject based on an operation by the subject (step S605).

[0102] Next, the electronic device 100 analyzes the measured pulse wave (step S606). Specifically, the electronic device 100 analyzes, for example, the rising index Sl, AI, and pulse rate PR related to the measured pulse wave.

[0103] The electronic device 100 estimates the postprandial lipid level of the subject by applying the rising index Sl, AI, and pulse rate PR analyzed in step S606 and the subject's age to the estimation formula created in the flow diagram of Fig. 14 (step S607). The estimated postprandial lipid level is notified to the subject by, for example, the notification unit 147 of the electronic device 100.

[0104] FIG. 16 is a diagram showing a comparison between postprandial lipid values ​​estimated using the estimation formula created by the flow of FIG. 14 and actually measured postprandial lipid values. In the graph shown in FIG. 16, the horizontal axis shows the measured postprandial lipid values ​​(actual measured values), and the vertical axis shows the estimated postprandial lipid values. The measured lipid values ​​were measured using Cobas b101 manufactured by Roche Diagnostics. As shown in FIG. 16, the measured values ​​and the estimated values ​​are generally within a range of ±20%. In other words, it can be said that the estimation accuracy using the estimation formula is within 20%.

[0105] In this way, the electronic device 100 can estimate the postprandial lipid level based on the preprandial and postprandial blood glucose levels measured by blood sampling from the subject.

[0106] Furthermore, the electronic device 100 estimates the lipid level using the pre-meal and post-meal blood glucose levels. Therefore, the electronic device 100 can estimate the lipid level by correcting (removing) the influence of the post-meal blood glucose level on the pulse wave. As a result, the electronic device 100 improves the accuracy of estimating the lipid level.

[0107] In this embodiment, the estimation formula is created using the blood glucose level, pulse wave, and lipid level before and after a meal, but the creation of the estimation formula is not limited to this, and the estimation formula may be created using either the blood glucose level, pulse wave, and lipid level before or after a meal. Furthermore, the electronic device 100 may estimate the lipid level of the subject at any timing, not limited to the postprandial lipid level. The electronic device 100 can also estimate the lipid level at any timing non-invasively and in a short time.

[0108] Similarly to the first embodiment, in the electronic device 100 according to this embodiment, the estimation formula stored in the storage unit 145 may be updated based on the subject's preprandial blood glucose level and pulse wave and postprandial blood glucose level and pulse wave acquired in steps S602 to S605 in estimating the lipid level. In this way, the estimation formula is updated every time the subject estimates the blood glucose level, improving the accuracy of estimating the postprandial lipid level using the estimation formula.

[0109] In the above first and second embodiments, an example has been described in which, when estimating a postprandial blood glucose level using the electronic device 100, the subject inputs a preprandial blood glucose level measured using a blood glucose meter using the input unit 141 of the electronic device 100. However, the preprandial blood glucose level may be automatically input to the electronic device 100 from, for example, the blood glucose meter.

[0110] 17 is a diagram illustrating a schematic diagram of communication between the electronic device 100 and the blood glucose meter 160. The blood glucose meter 160 includes a communication unit and can transmit and receive information via the communication unit 146 of the electronic device 100. For example, when the blood glucose meter 160 measures a blood glucose level (pre-prandial blood glucose level) based on an operation of a subject, the blood glucose meter 160 transmits the blood glucose level as a measurement result to the electronic device 100. The electronic device 100 estimates the post-prandial blood glucose level of the subject using the blood glucose level acquired from the blood glucose meter 160, for example, according to the flow shown in FIG. 10 or FIG. 13 or the like.

[0111] Similarly, in the third embodiment, the electronic device 100 may obtain the blood glucose level from the communicable blood glucose meter 160. In this case, the electronic device 100 can estimate the lipid level based on the blood glucose level obtained from the blood glucose meter 160.

[0112] In the above embodiment, an example has been described in which the estimation of blood glucose levels and lipid levels is performed by the electronic device 100, but the estimation of blood glucose levels and lipid levels does not necessarily have to be performed by the electronic device 100. An example in which the estimation of blood glucose levels and lipid levels is performed by a device other than the electronic device 100 will be described.

[0113] FIG. 18 is a schematic diagram showing a schematic configuration of a system according to an embodiment of the present invention. The system according to the embodiment shown in FIG. 18 includes an electronic device 100, a server 151, a mobile terminal 150, and a communication network. As shown in FIG. 18, the pulse wave measured by the electronic device 100 is transmitted to the server 151 through the communication network and stored in the server 151 as the subject's personal information. The server 151 estimates the subject's blood glucose level or lipid level by comparing with the subject's previously acquired information and various databases. The server 151 may further create advice optimal for the subject. The server 151 returns the estimation result and advice to the mobile terminal 150 owned by the subject. The mobile terminal 150 can report the received estimation result and advice from the display unit of the mobile terminal 150, thereby constructing a system. By utilizing the communication function of the electronic device 100, the server 151 can collect information from multiple users, thereby further improving the accuracy of the estimation. Moreover, since the mobile terminal 150 is used as a notification means, the electronic device 100 does not require the notification unit 147, and can be further miniaturized. Furthermore, since the blood glucose level or lipid level of the subject is estimated by the server 151, the computational burden on the control unit 143 of the electronic device 100 can be reduced. Furthermore, since the previously acquired information of the subject can be stored by the server 151, the burden on the storage unit 145 of the electronic device 100 can be reduced. Therefore, the electronic device 100 can be further miniaturized and simplified. Furthermore, the computational processing speed is improved.

[0114] Although the system according to the present embodiment has been shown to have a configuration in which electronic device 100 and mobile terminal 150 are connected via a communication network via server 151, the system according to the present invention is not limited to this. It may also be configured in such a way that electronic device 100 and mobile terminal 150 are directly connected via a communication network without using server 151.

[0115] Although the present invention has been described with respect to specific embodiments in order to fully and clearly disclose the present invention, the appended claims should not be construed as being limited to the above-described embodiments, but should be construed to embody all modifications and alternative arrangements that may be made by those skilled in the art within the scope of the basic subject matter presented herein.

[0116] For example, in the above embodiment, the sensor unit 130 is provided with the angular velocity sensor 131, but the electronic device 100 according to the present invention is not limited to this. The sensor unit 130 may be provided with an optical pulse wave sensor consisting of a light-emitting unit and a light-receiving unit, or may be provided with a pressure sensor. Furthermore, the electronic device 100 is not limited to being worn on the wrist. The sensor unit 130 may be placed on an artery on the neck, ankle, thigh, ear, or the like. [Explanation of symbols]

[0117] 100 Electronic equipment 110 Mounting part 120 Measuring section 120a Back 120b surface 111 Opening 130 Sensor section 131 Angular rate sensor 132 Vein Placement 133 Support part 140 Elastic Body 141 Input section 143 Control Unit 144 Power supply section 145 Storage section 146 Communications Department 147 Information Department 150 Mobile Devices 151 Server 160 Blood Glucose Meter

Claims

1. A method for generating an estimation model for estimating a blood glucose level or glucose metabolism of a subject based on a pulse wave acquired from the subject, comprising: A method for generating an estimation model, the estimation model being created based on sample data in which a pulse wave acquired from a subject is associated with a blood glucose level measured from the subject.

2. The method for generating an estimation model according to claim 1 , wherein the sample data is obtained from a plurality of subjects.

3. The method for generating an estimation model according to claim 1 or 2, wherein the subject is the subject.

4. The method for generating an estimation model according to claim 1 , wherein the sample data is data in which the blood glucose level variation is close to a normal distribution.

5. The method for generating an estimation model according to claim 1 , wherein the sample data is acquired at least either before or after a meal of the subject.

6. The method for generating an estimation model according to claim 1 , wherein the sample data uses a rise index SI based on an accelerated plethysmogram derived by differentiating the plethysmogram twice.

7. The method for generating an estimation model according to claim 1 , wherein the sample data uses AI based on a forward wave and a reflected wave of the pulse wave.

8. The method for generating an estimation model according to claim 7 , wherein the AI ​​is based on a forward wave of the pulse wave minus a reflected wave.

9. The method for generating an estimation model according to claim 1 , wherein the sample data uses a pulse rate PR calculated based on a period of the pulse wave.

10. The method for generating an estimation model according to claim 1 , wherein the estimation model is generated by a regression analysis based on the pulse wave that has been subjected to a fast Fourier transform.

11. The method for generating an estimation model according to claim 1 , wherein the sample data is data in which the pulse wave is measured by a pulse wave meter and the blood glucose level is measured by a blood glucose meter.

12. The method for generating an estimation model according to claim 1 , wherein the sample data is data obtained by measuring the blood glucose level by taking blood from the subject.

13. The method for generating an estimation model according to claim 1 , wherein the sample data further includes an age of the subject.

14. A computer is caused to execute creation of an estimation model for estimating a blood glucose level or glucose metabolism of a subject based on a pulse wave acquired from the subject, based on sample data in which a pulse wave acquired from the subject and a blood glucose level measured from the subject are associated with each other. program.

15. The program stores a program for causing a computer to execute the creation of an estimation model for estimating a blood glucose level or glucose metabolism of a subject based on a pulse wave acquired from the subject, based on sample data in which a pulse wave acquired from the subject and a blood glucose level measured from the subject are associated with each other. Recording medium.

16. and a control unit that creates an estimation model for estimating a blood glucose level or glucose metabolism of a subject based on a pulse wave acquired from the subject, based on sample data in which a pulse wave acquired from the subject and a blood glucose level measured from the subject are associated with each other. electronic equipment.

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